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Drury B. Crawley, Ph.D.FASHRAE, BEMP, FIBPSA, AIA

Bentley Systems, Inc.

3 March 2016

Portugal Chapter

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IndustrialTransportationResidentialCommercial

EIA 2013, Eurostat 2014

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Weather:the state of the atmosphere with respect to wind,

temperature, cloudiness, moisture, pressure, etc.

Climate: the composite or generally prevailing weather conditions of a region, as temperature, air pressure, humidity, precipitation, sunshine, cloudiness, and winds, throughout the year, averaged over a series of years.

www.dictionary.com

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3

Building design and performance modeling require weather data to represent climatic conditions of the building location, including:

Peak heating and cooling design conditions for load calculations (temperature, humidity, solar, and wind conditions for design calculations)

Building Performance Simulation Typical hourly weather data Actual hourly weather data for calibration to utility bills Future hourly weather data

In the past, usually only available from ground observing stations Rarely includes solar data Temperature/humidity data generally robust. Wind data is problematic – extremely variable due to terrain and site

obstructions. Other data can be limited or incomplete.

Now, more sources incorporate remote sensing (satellite) data. Accuracy is quite good. Advantage – comprehensive global data, relatively decent grid. Not dependant on ground stations.

In general master data sets such as those from US NCEI are near-real-time. Data posted within a few days.

But design conditions and data for simulation often require summary and further calculations before it can be used Design conditions based on historical period of record Ground observing stations (near real-time data) do not record solar

radiation.

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Used for design sizing of heating, ventilating, air-conditioning, and dehumidification equipment, as well as or other energy-related processes in residential, agricultural, commercial, and industrial applications.

Includes as a minimum dry-bulb, wet-bulb, and dew-point temperature, and wind speed with direction at various frequencies of occurrence.

Typical annual percentiles* used: 99.6% heating dry-bulb temperature and

1% cooling dry-bulb temperature with coincident wet-bulb.

Depending on the application, use other percentiles (99, 0.4, 2, 5) or variables (wind speed, dewpoint, wetbulb, etc.). Monthly cooling percentiles also available (0.4, 2, 5, 10). *Percentiles represent number of hours that the design condition can be expected to be exceeded in a typical year, based on15-30 years of data. 99.6% ≈ 35 annual hours (8760 – (99.6% * 8760)). 1% ≈ 88 annual hours (8760 – (1% * 8760)).

Best source for design conditions: Chapter 14 Climatic Design Information, ASHRAE Handbook-Fundamentals: 6,443 locations through the world Integrated Surface Dataset (ISD) data for stations

from around the world provided by NCDC for the period 1982 to 2010

Updated every four years

Climate changing! Comparing design conditions for 1274 locations between 1977-1986 and 1997-2006: 99.6% annual dry-bulb temperature increased 1.52°C 0.4% annual dry-bulb increased 0.79°C annual dew point increased by 0.55°C HDD base 18.3°C) decreased 237°C-days, CDD base 10°C increased 136°C-days.

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Difference in 99.6% Heating Dry Bulb Temperature (°C)

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Difference in 99.6% Heating Dry Bulb Temperature (°F)

Average:1.52°C / 2.74°F

6.9919861977

6.9920061997

6.99 DBDBDB

99.6% heating dry bulb temperature + 0.76 °C + 1.37 °F

0.4% cooling dry bulb temperature + 0.38 °C + 0.68 °F

0.4% dehum. dew point temperature + 0.28 °C + 0.50 °F

Dry bulb temperature range ~ 0 °C ~ 0 °F

Average temperature + 0.41 °C + 0.73 °F

Heating degree-days base 18.3°C / 65°F - 118 °C-day - 212 °F-day

Cooling degree-days base 10°C / 50°F + 68 °C-day + 122 °F-day

14

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2013 ASHRAE Handbook - Fundamentals (SI) © 2013 ASHRAE, Inc.

WMO#: 722190

Lat: 33.64N Long: 84.43W Elev: 313 StdP: 97.62 Time Zone: -5.00 (NAE) Period: 86-10 WBAN: 13874

Annual Heating and Humidification Design Conditions

99.6% 99% DP HR MCDB DP HR MCDB WS MCDB WS MCDB MCWS PCWD

( a ) ( b ) ( c ) ( d ) ( e ) ( f ) ( g ) ( h ) ( i ) ( j ) ( k ) ( l ) ( m ) ( n ) ( o )

(1) 1 -5.8 -3.1 -15.5 1.0 -1.9 -12.7 1.3 0.1 11.1 4.4 10.5 4.4 5.3 320 (1)

Annual Cooling, Dehumidification, and Enthalpy Design Conditions

DB MCWB DB MCWB DB MCWB WB MCDB WB MCDB WB MCDB MCWS PCWD

( a ) ( b ) ( c ) ( d ) ( e ) ( f ) ( g ) ( h ) ( i ) ( j ) ( k ) ( l ) ( m ) ( n ) ( o ) ( p )

(2) 7 9.4 34.4 23.4 33.1 23.3 32.1 23.0 25.2 31.4 24.7 30.4 24.1 29.4 3.9 300 (2)

DP HR MCDB DP HR MCDB DP HR MCDB Enth MCDB Enth MCDB Enth MCDB

( a ) ( b ) ( c ) ( d ) ( e ) ( f ) ( g ) ( h ) ( i ) ( j ) ( k ) ( l ) ( m ) ( n ) ( o ) ( p )

(3) 23.5 19.0 27.4 23.0 18.4 26.8 22.6 17.9 26.5 78.3 31.4 76.0 30.4 74.0 29.8 800 (3)

Extreme Annual Design Conditions

1% 2.5% 5% Min Max Min Max Min Max Min Max Min Max Min Max

( a ) ( b ) ( c ) ( d ) ( e ) ( f ) ( g ) ( h ) ( i ) ( j ) ( k ) ( l ) ( m ) ( n ) ( o ) ( p )

(4) 9.6 8.5 7.7 28.0 -10.0 35.9 2.5 1.8 -11.7 37.3 -13.2 38.3 -14.5 39.4 -16.3 40.7 (4)

Monthly Climatic Design Conditions

Annual Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Hours8 to 4 &

12.8/20.6

2%

Coldest month WS/MCDB

2%

MCWS/PCWDto 99.6% DB0.4%

1%

1%

MCWS/PCWDto 0.4% DB

Enthalpy/MCDB1% 2%0.4%

ColdestMonth

HottestMonth

Humidification DP/MCDB and HR99.6% 99%

Heating DB

0.4% 0.4%

Dehumidification DP/MCDB and HR

ExtremeMaxWB

Extreme Annual DB n-Year Return Period Values of Extreme DBn=20 years n=50 years

ATLANTA MUNICIPAL, GA, USA

HottestMonth

DB Range

Cooling DB/MCWB Evaporation WB/MCDB

Mean Standard deviation n=5 years

0.4%

Extreme Annual WSn=10 years

1%

1% 2%

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Climatic data needed for simulating representative performance from a single year analysis.

TMY (Typical Meteorological Year) approach is most widely used– a composite of months (not all from same year), each representative for the period of record.

Months selected using statistical of indices (daily min, mean, max) dry-bulb temperature, dew-point temperature, wind speed, and total global and direct solar radiation. Each method varies the weightings of the indices based on their importance.

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Actual hourly weather data required to calibrate to utility bills in existing buildings and subsequent evaluation of retrofit alternatives.

Many sources – some providing near-real time data and/or prediction: US NCEI WMO Data Center Weather Underground Weather.com Weather Source

Biggest issue – how complete are the data – does it include temperature, humidity, wind, solar radiation?

USA_VA_Sterling-Washington.DullesUSA_VA_Sterling-Washington.Dulles

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Heating:GasHeating:GasReheat:GasReheat:GasCooling:ElectricityCooling:ElectricityFans:ElectricityFans:ElectricityInterior Lights:ElectricityInterior Lights:ElectricityPlug Loads:ElectricityPlug Loads:ElectricityDHW:GasDHW:Gas

USA_VA_Sterling-Washington.Dulles

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Heating:GasReheat:GasCooling:ElectricityFans:ElectricityInterior Lights:ElectricityPlug Loads:ElectricityDHW:Gas

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CIBSE UK only, 14 locations Derived from reanalysis (downscale of gridded

latitude/longitude) from IPCC (UKCP09) 2020, 2050, 2080 Low, medium, high cases See CIBSE TM48

CCWorldWeatherGen Utility converts an EPW into a IPCC A1FI scenario

morphed file.

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Methods Dynamic downscaling Physics-based model used to downscale global climate model results

Analogue scenarios Find existing locations with comparable data to the predicted climate change scenario

results

Time series adjustment (morphing) Shift and stretch the existing data to match the predicted monthly change

Statistical models Stochastic model trained on observed data adjusts data based on altered frequency

distributions of weather variables

Average monthly temperature change (+) Diurnal temperature change (+/-) Cloud cover decrease (more solar radiation) Precipitation changes (+/-)

Los Angeles CA USA TMY2 Autumn Novermber 23 Climate ChangeLos Angeles CA USA TMY2 Autumn Novermber 23 Climate Change

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Los Angeles CA USA TMY2 Autumn Novermber 23 Climate Change

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Base Diurnal Temperature

Scenario Diurnal Temperature (+/-)

Scenario Delta Temperature (+)

Night-time Temperatures Remain Higher

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Heat Island size and temperature are function of city population

Range is roughly 1-5 C Beyond 5 million people, roughly 5 C Depresses daytime temperature Raises nighttime temperature

Sterling VA (Washington Dulles Airport) TMY2 Spring April 10 Heat Island Sterling VA (Washington Dulles Airport) TMY2 Spring April 10 Heat Island

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Temperatures Remain Higher after Sunset

Temperatures Remain Higher until Sunrise

Small, 360 m2, two story office building 14 m2/person Lighting at 13 W/m2

Office Equipment at 8.5 W/m2

Natural gas heating and hot water Electric packaged cooling units Building fabric (opaque and windows) and equipment efficiencies

roughly equivalent to current minimum regulations (ASHRAE Standard 90.1)

US national average pollution emission factors Simulated using EnergyPlus

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USA_VA_Sterling-Washington.Dulles

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Heating:GasHeating:GasReheat:GasReheat:GasCooling:ElectricityCooling:ElectricityFans:ElectricityFans:ElectricityInterior Lights:ElectricityInterior Lights:ElectricityPlug Loads:ElectricityPlug Loads:ElectricityDHW:GasDHW:Gas

USA_VA_Sterling-Washington.DullesUSA_VA_Sterling-Washington.Dulles

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Heating:GasReheat:GasCooling:ElectricityFans:ElectricityInterior Lights:ElectricityPlug Loads:ElectricityDHW:Gas

Predicted Monthly Primary Energy End-Use Consumption, in MJ/m2, in Washington, DC, USA for Baseline and Four Climate Change Scenarios

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USA_VA_Sterling-Washington.Dulles Typical Year Standard and Climate Change Scenarios, Source Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

3/3/2016

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2)

USA_VA_Sterling-Washington.Dulles Standard and Climate Change, Site Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

A1FI A2 B1 B2

End

-Use

Ene

rgy

Con

sum

ptio

n (M

egaj

oule

s/m

2)

USA_VA_Sterling-Washington.Dulles Standard and Climate Change, Source Energy Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

3/3/2016

16

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

Low

TM

Y2

Hig

h

A1FI A2 B1 B2

End

-Use

Ene

rgy

Con

sum

ptio

n (M

egaj

oule

s/m

2)

USA_VA_Sterling-Washington.Dulles Low Energy and Climate Change, Source Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

1,700

1,800

1,900

2,000

Low

IWE

C

Hig

h

Low

IWE

C

Hig

h

Low

IWE

C

Hig

h

Low

IWE

C

Hig

h

Low

IWE

C

Hig

h

A1FI A2 B1 B2

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

SGP_Singapore Standard and Climate Change, Source Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

3/3/2016

17

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

1,700

1,800

1,900

2,000

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

A1FI A2 B1 B2

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

CAN_ON_Toronto Standard and Climate Change, Site Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

1,700

1,800

1,900

2,000

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

A1FI A2 B1 B2

End

-Use

Ene

rgy

Con

sum

ptio

n (M

egaj

oule

s/m

2)

CAN_ON_Toronto Standard and Climate Change, Source Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

3/3/2016

18

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

A1FI A2 B1 B2

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

CAN_ON_Toronto Low Energy and Climate Change, Source Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

1,700

1,800

1,900

2,000

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

HtIsLo HtIsHi

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

CAN_ON_Toronto Standard and Heat Island, Source Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

3/3/2016

19

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

HtIsLo HtIsHi

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

CAN_ON_Toronto Low Energy and Heat Island, Source Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

Predicted Monthly Primary Energy End-Use Consumption, in MJ/m2, in Toronto, ON, CAN for Baseline and Four Climate Change Scenarios

-5

5

15

25

35

45

55

65

75

Jan

Feb

Mar

Ap

rM

ayJu

nJu

lA

ug

Se

pO

ctN

ovD

ec Jan

Feb

Mar

Ap

rM

ayJu

nJu

lA

ug

Se

pO

ctN

ovD

ec Jan

Feb

Mar

Ap

rM

ayJu

nJu

lA

ug

Se

pO

ctN

ovD

ec Jan

Feb

Mar

Ap

rM

ayJu

nJu

lA

ug

Se

pO

ctN

ovD

ec Jan

Feb

Mar

Ap

rM

ayJu

nJu

lA

ug

Se

pO

ctN

ovD

ec

CWEC CWEC CWEC CWEC CWEC

A1FI A2 B1 B2

LowEn LowEn LowEn LowEn LowEn

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

CAN_ON_Toronto Typical Year Low Energy and Climate Change Scenarios, Source Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

3/3/2016

20

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

1,700

1,800

1,900

2,000

2,100

2,200

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

Low

CW

EC

Hig

h

A1FI A2 B1 B2

En

d-U

se E

ner

gy

Co

nsu

mp

tio

n (

Meg

ajo

ule

s/m

2 )

CAN_NU_Resolute Standard and Climate Change, Source Energy

Heating:Gas

Reheat:Gas

Cooling:Electricity

Fans:Electricity

Lights:Electricity

Plug Loads:Electricity

SHW:Gas

0

20

40

60

80

100

120

140

Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec

TMY2 TMY2 TMY2 TMY2 TMY2

A1FI A2 B1 B2

Std Std Std Std Std

En

d-U

se E

ne

rgy

Co

nsu

mp

tion

(M

eg

ajo

ule

s/m

2)

USA_VA_Sterling-Washington.Dulles Typical Year Standard and Climate Change Scenarios, Source EnergyHeating:Gas

Reheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

0

20

40

60

80

100

120

140

Jan

Fe

bM

arA

prM

ay Jun

Jul

Aug

Sep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ay Jun

Jul

Aug

Sep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ay Jun

Jul

Aug

Sep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ay Jun

Jul

Aug

Sep Oct

Nov

Dec Jan

Fe

bM

arA

prM

ay Jun

Jul

Aug

Sep Oct

Nov

Dec

TMY2 TMY2 TMY2 TMY2 TMY2

A1FI A2 B1 B2

Std Std Std Std Std

En

d-U

se E

ne

rgy

Co

nsu

mp

tion

(M

eg

ajo

ule

s/m

2)

USA_CA_Los.Angeles Typical Year Standard and Climate Change Scenarios, Source Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

0

20

40

60

80

100

120

140

Jan

Fe

bM

arA

pr

May

Jun

Jul

Au

gS

ep

Oct

No

vD

ec

Jan

Fe

bM

arA

pr

May

Jun

Jul

Au

gS

ep

Oct

No

vD

ec

Jan

Fe

bM

arA

pr

May

Jun

Jul

Au

gS

ep

Oct

No

vD

ec

Jan

Fe

bM

arA

pr

May

Jun

Jul

Au

gS

ep

Oct

No

vD

ec

Jan

Fe

bM

arA

pr

May

Jun

Jul

Au

gS

ep

Oct

No

vD

ec

IWEC IWEC IWEC IWEC IWEC

A1FI A2 B1 B2

Std Std Std Std Std

En

d-U

se

En

erg

y C

on

su

mp

tio

n

(Me

ga

jou

les

/m2 )

SGP_Singapore Typical Year Standard and Climate Change Scenarios, Source Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

-5

5

15

25

35

45

55

65

75

Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec

Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec

Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec

Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec

Jan

Fe

bM

arA

prM

ayJu

nJu

lA

ugS

ep Oct

Nov

Dec

CWEC CWEC CWEC CWEC CWEC

A1FI A2 B1 B2

LowEn LowEn LowEn LowEn LowEn

En

d-U

se

En

erg

y C

on

su

mp

tio

n

(Me

ga

jou

les

/m2 )

CAN_ON_Toronto Typical Year Low Energy and Climate Change Scenarios, Source Energy

Heating:GasReheat:GasCooling:ElectricityFans:ElectricityLights:ElectricityPlug Loads:ElectricitySHW:Gas

3/3/2016

21

Climatic design data is critical for building design (equipment and systems sizing)

For building performance simulation, typical (TMY), actual, and future weather can all support building evaluation Some question of whether single TMY is enough (research on XMYs underway) Rich resources of data now available – both ground observing stations and satellite data.

Climate change scenarios can be represented today by modifying existing hourly weather files Buildings in higher latitude climates (north and south) will likely see decreases (heating

decreases more than cooling increases) Buildings in tropical and semi-tropical locations will see increases – but lower than changes

in higher latitudes – primarily due to increased cooling Energy-efficient buildings mitigate most impacts of both climate change and heat islands.

3/3/2016

22

Dru Crawley

Dru.Crawley@Bentley.com

@DruCrawley

DruCrawley

Building Performance Simulation for Design and Operation www.routledge.com/books/details/9780415474146/

ASHRAE Handbook—Fundamentals 2013 www.ashrae.org

NCDC Integrated Surface Data Documentation: ftp://ftp.ncdc.noaa.gov/pub/data/techrpts/tr200101/tr2001-01.pdfData: ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00532-TAP-A0001

Typical Meteorological Year Data Sets (Climate.OneBuilding free weather data in EPW, ESP-r and Radiance formats) climate.onebuilding.org/

Drury B. Crawley. 1998. “Which Weather Data Should You Use for Energy Simulations of Commercial Buildings?” in ASHRAE Transactions, pp. 498-515, Vol. 104, Pt. 2. Atlanta: ASHRAE. climate.onebuilding.org/papers/1998_06_Crawley_Which_Weather_Data_Should_You_Use_for_Energy_Simulations_of_Commercial_Buildings.pdf

Crawley, Drury B. 2008. “Estimating the Impacts of Climate Change and Urbanization on Building Performance,” Building Performance Simulation, pp. 91-115, Vol. 1, No. 2 (June). climate.onebuilding.org/papers/2008_06_Crawley_Estimating_the_impacts_of_climate_change_and_urbanization_on_building_performance.pdf

Meteonorm www.meteonorm.com

Weather Underground www.weatherunderground.com

National Center for Environmental Information lwf.ncdc.noaa.gov/oa/climate/climatedata.html

CIBSE Technical Manual 48 (TM48), Use of climate change scenarios for building simulation: the CIBSE future weather years www.cibse.org/index.cfm?go=publications.view&item=449

Climate Change World Weather Generator (CCWorldWeatherGen) www.energy.soton.ac.uk/ccworldweathergen/

Dview (tool for displaying and comparing weather data (and CSV data) beopt.nrel.gov/downloadDView

3/3/2016

23

Meteonorm Allows users to create a TMY-type file for any location in the world. Interpolates from observing stations and statistics. Does include TRY (European TMY-type files) for a number of locations Useful where no other data exists

Other sources! IWEC2 with 3012 locations worldwide (outside US/Canada) available from ASHRAE since early 2012. EU Energy Performance Directive requiring simulation, new data sets for Estonia, Finland, Ireland, Slovenia California Climate Zones update… Australia RMY update… ISHRAE update… Others.

BOTTOM LINE: Know what you’re using – the provenance, source, representativeness – before depending on it. Better to test against another file you’ve used with similar climatic conditions.